Assessment 3 Alicia Phan

Exercise 1 - Reproducing and arranging ggplot2 figures

Exercise 2 - Visualizing key characteristics

Exercise 3 - Improving ggplot figure

Exercise 4 - Exploring relationships through visualizations

References

Exercise 1 - Reproducing and arranging ggplot2 figures

Exercise 2 - Visualizing key characteristics

Exercise 3 - Improving ggplot figure

Exercise 4 - Exploring relationships through visualizations

References

Exercise 1 - Reproducing and arranging ggplot2 figures

Histogram of age by gender

Percentage of gender by ethnicity

Please refer to the combined plot below showing the two methods used. The two methods are easy to use to combine multiple plots to one plot. However, there were several additions added to the original plot to ensure readability of the plots. For both methods, the x axis for the second plot showing ethnicity showed some overlapping text due to the plot size when combined. From this, label_wrap function was used to wrap the x labels and ensure readability. The ggarrange method shows easier way to combine the two plots with shared legend, the two plots here shared the legend of gender column, which is faciliated through the field “common.legend = TRUE” field. In comparison, the plot_grid function from cowplot package does not have this field; however, there is a solution to add common legend by extracting the legend.

Using plot_grid() function from cowplot package

Using ggarrange() function from ggpubr package

Exercise 2 - Visualizing key characteristics

Visualisations of the following variables: Age, gender, ethnicity variables

Interactive table

## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

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	V1	patient_id	bp_comment	systolic_bp_1	diastolic_bp_1	systolic_bp_2	diastolic_bp_2	systolic_bp_3	diastolic_bp_3	systolic_bp_4	diastolic_bp_4	wave_id	gender	age	ethnicity_1	ethnicity_2	education	marital_status	family_size	family_income	age_cat

	V1	patient_id	systolic_bp_1	diastolic_bp_1	systolic_bp_2	diastolic_bp_2	systolic_bp_3	diastolic_bp_3	wave_id	gender	age	ethnicity_1	ethnicity_2	education	marital_status	family_size	family_income	age_cat
1	1	73557	122	72	114	76	102	74	2015	Male	69	Black	Black	High School	Separated	3	$15,000 - $19,999	60+
2	2	73558	156	62	160	80	156	42	2015	Male	54	White	White	High School	Married	4	$35,000 - $44,999	40-59
3	3	73559	140	90	140	76	146	80	2015	Male	72	White	White	Some college	Married	2	$65,000 - $74,999	60+
4	4	73560	108	38	102	34	104	38	2015	Male	9	White	White			4	$55,000 - $64,999	0-17
5	5	73561	136	86	134	88	142	86	2015	Female	73	White	White	College graduate	Married	2	$100,000 or more	60+
6	6	73562	160	84	158	82	154	80	2015	Male	56	Mexican	Mexican	Some college	Divorced	1	$55,000 - $64,999	40-59
7	7	73563							2015	Male	0	White	White			3	$100,000 or more	0-17
8	8	73564	118	80	124	80	126	82	2015	Female	61	White	White	College graduate	Widowed	1	$65,000 - $74,999	60+
9	9	73566	128	74	124	72	114	72	2015	Female	56	White	White	High School	Divorced	7 or more	$15,000 - $19,999	40-59
10	10	73567	140	78	142	78	142	76	2015	Male	65	White	White	9-11th grade	Widowed	1	$10,000 - $14,999	60+

Showing 1 to 10 of 29,400 entries

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Age

Gender

Ethnicity variables

a. Ethnicity 1

b. Ethnicity 2

ggsave("age_combined.png", age_combined)
ggsave("gender_combined.png", gender_combined)
ggsave("ethnicity1.png", ethnicity1_combined)
ggsave("ethnicity2.png", ethnicity2_combined)

Based on the results of both variable, it is shown that there were 5000 observation of “other” category in the first variable, whereas only around 1000 observations of “other” category in the second one. From this, it is more preferable to select the variable Ethnicity 2 to include more details counts of the ethnicity variable that includes Asian category. The Ethnicity 2 variable is able to explain more of the data. Therefore, the ethnicity 1 variable will be removed.

nhanes_data <- nhanes_data %>%
  select(-ethnicity_1)

Exercise 3 - Improving ggplot figure

For interpretation of the gragh, the graph is missing descriptive elements like title of gragh, legend of the graph, weight measurement value. In addition, there were too many colors used for the graph for each participant; the colors used were rainbow-color, which might not be friendly for color deficiency readers.

For visual representation, the area for the color is very minimal for each participant, and there is overlapping between lines. In addition, the background grid has too many lines. In addition, the lines are too thin as well, make it hard for line visibility.

Regarding data completeness, it seems that the legend for participant 1 is missing. This is due to the length of the legend that does not fit the plot. This also causes the confusion and the completeness of the data when presented in the plot.

The following suggestions are proposed:

Title, Legend name, and weight measurement value should be added.
Background grid should be simplified.
If possible, the information on how much the participant increase/decrease can be shown, and that information on each participant should be available. Proposition on creating categories of increase/decrease from beginning to end for each participants.

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Exercise 4 - Exploring relationships through visualizations

The report aims to understand the relationships between age, gender, and systolic blood pressure (SBP). In order to explore the relationships between variables, it is important to verify the data completeness and the data itself. /

Below is a summary of the NHANES data that will be used for analysis showing the missing data and completion rate of each variable. Based on the data, we noted that there are four SBP measurements representing different time of measurement. In addition, the SBP measurements include missing data where the completion rates are 70%, 72.3%, 72.1%, and 4.7% for measurements from wave 1 to 4 respectively. Furthermore, both gender and age variables have completion rate of 100% meaning no missing data.

Data summary
Name	nhanes_data_analysis_raw
Number of rows	29400
Number of columns	7
_______________________
Column type frequency:
character	1
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gender	0	1	4	6	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
patient_id	0	1.00	88256.50	8487.19	73557	80906.75	88256.5	95606.25	102956	▇▇▇▇▇
systolic_bp_1	8784	0.70	119.94	18.91	66	106.00	116.0	130.00	236	▂▇▂▁▁
systolic_bp_2	8120	0.72	119.99	19.11	66	106.00	116.0	130.00	238	▂▇▂▁▁
systolic_bp_3	8182	0.72	119.66	18.87	62	106.00	116.0	130.00	232	▁▇▂▁▁
systolic_bp_4	28007	0.05	128.16	23.81	76	110.00	126.0	144.00	234	▃▇▃▁▁
age	0	1.00	32.52	24.91	0	10.00	28.0	54.00	80	▇▃▃▃▃

Based on the preliminary data completeness analysis above, the data transformation was conducted to take average of all four SBP measurements to obtain a new SBP variable as average SBP. Regarding the average SBP variable, the variable is missing only if four of the measurements are missing. Subsequently, in order to ensure the quality of analysis, we filtered out the observations where average SBP variable, age or gender variable is missing. Noted that the age and gender variables are completed per above observation, therefore, we only filtered out observations where average SBP is missing.

nhanes_complete <- nhanes_data_analysis_raw %>%
  mutate(avg_sbp = rowMeans(select(nhanes_data_analysis_raw, systolic_bp_1, 
                                   systolic_bp_2, systolic_bp_3, systolic_bp_4)
                            , na.rm = TRUE)) %>%
  filter(!is.na(avg_sbp)) %>%
  select(patient_id, age, gender, avg_sbp)

skim(nhanes_complete)

Data summary
Name	nhanes_complete
Number of rows	21604
Number of columns	4
_______________________
Column type frequency:
character	1
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
gender	0	1	4	6	0	2	0

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
patient_id	1	88206.39	8461.65	73557.00	80869.75	88179.50	95496.75	102956	▇▇▇▇▇
age	1	39.92	22.34	8.00	18.00	39.00	60.00	80	▇▅▅▅▅
avg_sbp	1	120.07	18.94	64.67	106.67	116.67	130.00	234	▁▇▂▁▁

After the data cleaning, 7796 observations were removed from the original dataset to remove the missing data. Skim() function was used to ensure the data completeness of the three variables: Age, gender, avg_sbp.

In order to further understand the dataset, we need to explore the distribution of each of the variable.

We noted that average SBP is a continuous numeric variable with right skewed distribution. However, looking at only the distribution, there is limited information on where the value of SBP is considered normal. From this, a new categorical variable is created to classify the average SBP observations to the level severity as follows:

Normal SBP < 120 mm Hg
Elevated: 120 ≤ SBP ≤ 129 mm Hg
Stage 1 hypertension: 130 ≤ SBP ≤ 139 mm Hg
Stage 2 hypertension: SBP ≥ 140 mm Hg

Finally, we will explore the relationships between age, gender, and blood pressure diagnosis. We first compared the SBP diagnosis with gender facet. There is an apparent trend that there are more observations for Elevated, Stage 1 and Stage 2 hypertension diagnosis for older age groups than younger ones starting at 50 and 60 years old. We also observed that the distribution of Elevated diagnosis shows less left skew for Male category than Female category. This means that there are more observations that male patients have higher blood pressure at the age of 20-30 years old. Similarly, Stage 1 hypertension for male is also less left skewed comparing to women, with more observations of stage 1 hypertension diagnosis for men than women at around 20-30 years old.

Secondly, we compared again age and gender but with SBP diagnosis as facet. This further confirmed our observations above where there are more male observations with Elevated diagnosis and Stage 1 hypertension at around 20-30 years old, and more female observations with normal diagnosis at the same age group. In contrast, for the age group from 50 years old and older, there is no notable pattern observed between the genders.

In conclusion, we can see that there are more Elevated, Stage 1 and Stage 2 diagnosis for older age groups. In addition, there is a notable observation where male patients have higher blood pressure for younger age groups around 20 - 30 years old.